SoroushVahidi/scidocs huggingface dataset:
Mitigating Sybils in Federated Learning Poisoning
(2018)

Machine learning (ML) over distributed multiparty data is required for a variety of domains. Existing approaches, such as federated learning, collect the outputs computed by a group of devices at a central aggregator and run iterative algorithms to train a globally shared model. Unfortunately, such approaches are susceptible to a variety of attacks, including model poisoning, which is made substantially worse in the presence of sybils. In this paper we first evaluate the vulnerability of federated learning to sybil-based poisoning attacks. We then describe FoolsGold, a novel defense to this problem that identifies poisoning sybils based on the diversity of client updates in the distributed learning process. Unlike prior work, our system does not bound the expected number of attackers, requires no auxiliary information outside of the learning process, and makes fewer assumptions about clients
An ensemble classifier with random projection for predicting multi-label protein subcellular localization
(2013)

In protein subcellular localization prediction, a predominant scenario is that the number of available features is much larger than the number of data samples. Among the large number of features, many of them may contain redundant or irrelevant information, causing the prediction systems suffer from overfitting. To address this problem, this paper proposes a dimensionality-reduction method that applies random projection (RP) to construct an ensemble multi-label classifier for predicting protein subcellular localization. Specifically, the frequencies of occurrences of gene-ontology terms are used as feature vectors, which are projected onto lower-dimensional spaces by random projection matrices whose elements conform to a distribution with zero mean and unit variance. The transformed low-dimensional vectors are classified by an ensemble of one-vs-rest multi-label support vector machine (SVM) classifiers, each corresponding to one of the RP matrices. The scores obtained from the ensemble are then fused for making the final decision. Experimental results on two recent datasets suggest that the proposed method can reduce the dimensions by six folds and remarkably improve the classification performance.
Novel Control Method for Multimodule PV Microinverter With Multiple Functions
(2018)

This paper presents a novel control method for multimodule photovoltaic microinverter (MI). The proposed MI employs a two-stage topology with active-clamped current-fed push–pull converter cascaded with a full-bridge inverter. This system can operate in grid-connected mode to feed power to the grid with a programmable power factor. This system can also operate in line-interactive mode, i.e., share load power without feeding power to the grid. In the event of grid power failure, the MI can operate in a standalone mode to supply uninterruptible power to the load. This paper presents a multiloop control scheme with power programmable capability for achieving the above multiple functions. In addition, the proposed control scheme embedded a multimodule parallel capability that multiple MI modules can be paralleled to enlarge the capacity with autonomous control in all operation modes. Finally, three 250-W MI modules are adopted to demonstrate the effectiveness of the proposed control method in simulations as well as experiments.
A refined PWM scheme for voltage and current source converters
(1990)

A pulse-width-modulation (PWM) scheme that uses the converter switching frequency to minimize unwanted load current harmonics is described. This results in the reduction of the number of switch communications per cycle. The method is suitable for high-performance variable-speed AC-motor drives that require high-output switching frequencies for near-silent operation. It is also applicable, without change, to voltage or current-source inverters and to two and four-quadrant three-phase PWM rectifiers. Key predicted results have been verified experimentally on a 5 kVA inverter breadboard.<<ETX>>
Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization
(2009)

Military operations are turning to more complex and advanced automation technologies for minimum risk and maximum efficiency.A critical piece to this strategy is unmanned aerial vehicles. Unmanned aerial vehicles require the intelligence to safely maneuver along a path to an intended target and avoiding obstacles such as other aircrafts or enemy threats. This paper presents a unique three-dimensional path planning problem formulation and solution approach using particle swarm optimization. The problem formulation was designed with three objectives: 1) minimize risk owing to enemy threats, 2) minimize fuel consumption incurred by deviating from the original path, and 3) fly over defined reconnaissance targets. The initial design point is defined as the original path of the unmanned aerial vehicles. Using particle swarm optimization, alternate paths are generated using B-spline curves, optimized based on the three defined objectives. The resulting paths can be optimized with a preference toward maximum safety, minimum fuel consumption, or target reconnaissance. This method has been implemented in a virtual environment where the generated alternate paths can be visualized interactively to better facilitate the decision-making process. The problem formulation and solution implementation is described along with the results from several simulated scenarios demonstrating the effectiveness of the method.
Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks
(2015)

Complex biological systems have been successfully modeled by biochemical and genetic interaction networks, typically gathered from high-throughput (HTP) data. These networks can be used to infer functional relationships between genes or proteins. Using the intuition that the topological role of a gene in a network relates to its biological function, local or diffusionbased “guilt-by-association” and graph-theoretic methods have had success in inferring gene functions. Here we seek to improve function prediction by integrating diffusion-based methods with a novel dimensionality reduction technique to overcome the incomplete and noisy nature of network data. In this paper, we introduce diffusion component analysis (DCA), a framework that plugs in a diffusion model and learns a low-dimensional vector representation of each node to encode the topological properties of a network. As a proof of concept, we demonstrate DCA’s substantial improvement over state-of-the-art diffusion-based approaches in predicting protein function from molecular interaction networks. Moreover, our DCA framework can integrate multiple networks from heterogeneous sources, consisting of genomic information, biochemical experiments and other resources, to even further improve function prediction. Yet another layer of performance gain is achieved by integrating the DCA framework with support vector machines that take our node vector representations as features. Overall, our DCA framework provides a novel representation of nodes in a network that can be used as a plug-in architecture to other machine learning algorithms to decipher topological properties of and obtain novel insights into interactomes. This paper was selected for oral presentation at RECOMB 2015 and an abstract is published in the conference proceedings. ar X iv :1 50 4. 02 71 9v 1 [ qbi o. M N ] 1 0 A pr 2 01 5
Experiments on deep learning for speech denoising
(2014)

In this paper we present some experiments using a deep learning model for speech denoising. We propose a very lightweight procedure that can predict clean speech spectra when presented with noisy speech inputs, and we show how various parameter choices impact the quality of the denoised signal. Through our experiments we conclude that such a structure can perform better than some comparable single-channel approaches and that it is able to generalize well across various speakers, noise types and signal-to-noise ratios.
Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media
(2018)

Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.
Fast Min-Sum Algorithms for Decoding of LDPC over GF(q)
(2006)

In this paper, we present a fast min-sum algorithm for decoding LDPC codes over GF(q). Our algorithm is different from the one presented by David Declercq and Marc Fossorier (2005) only at the way of speeding up the horizontal scan in the min-sum algorithm. The Declercq and Fossorier's algorithm speeds up the computation by reducing the number of configurations, while our algorithm uses the dynamic programming instead. Compared with the configuration reduction algorithm, the dynamic programming one is simpler at the design stage because it has less parameters to tune. Furthermore, it does not have the performance degradation problem caused by the configuration reduction because it searches the whole configuration space efficiently through dynamic programming. Both algorithms have the same level of complexity and use simple operations which are suitable for hardware implementations
Machine Learning Algorithms for Characterization of EMG Signals
(2014)

—In the last decades, the researchers of the human arm prosthesis are using different types of machine learning algorithms. This review article firstly gives a brief explanation about type of machine learning methods. Secondly, some recent applications of myoelectric control of human arm prosthesis by using machine learning algorithms are compared. This study presents two different comparisons based on feature extraction methods which are time series modeling and wavelet transform of EMG signal. Finally, of characterization of EMG for of human arm prosthesis have been and discussed.
Generating Haptic Textures with a Vibrotactile Actuator
(2017)

Vibrotactile actuation is mainly used to deliver buzzing sensations. But if vibrotactile actuation is tightly coupled to users' actions, it can be used to create much richer haptic experiences. It is not well understood, however, how this coupling should be done or which vibrotactile parameters create which experiences. To investigate how actuation parameters relate to haptic experiences, we built a physical slider with minimal native friction, a vibrotactile actuator and an integrated position sensor. By vibrating the slider as it is moved, we create an experience of texture between the sliding element and its track. We conducted a magnitude estimation experiment to map how granularity, amplitude and timbre relate to the experiences of roughness, adhesiveness, sharpness and bumpiness. We found that amplitude influences the strength of the perceived texture, while variations in granularity and timbre create distinct experiences. Our study underlines the importance of action in haptic perception and suggests strategies for deploying such tightly coupled feedback in everyday devices.
Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management
(2018)

Learning from sparse and delayed reward is a central issue in reinforcement learning. In this paper, to tackle reward sparseness problem of task oriented dialogue management, we propose a curriculum based approach on the number of slots of user goals. This curriculum makes it possible to learn dialogue management for sets of user goals with large number of slots. We also propose a dialogue policy based on progressive neural networks whose modules with parameters are appended with previous parameters fixed as the curriculum proceeds, and this policy improves performances over the one with single set of parameters.
A system for understanding imaged infographics and its applications
(2007)

Information graphics, or infographics, are visual representations of information, data or knowledge. Understanding of infographics in documents is a relatively new research problem, which becomes more challenging when infographics appear as raster images. This paper describes technical details and practical applications of the system we built for recognizing and understanding imaged infographics located in document pages. To recognize infographics in raster form, both graphical symbol extraction and text recognition need to be performed. The two kinds of information are then auto-associated to capture and store the semantic information carried by the infographics. Two practical applications of the system are introduced in this paper, including supplement to traditional optical character recognition (OCR) system and providing enriched information for question answering (QA). To test the performance of our system, we conducted experiments using a collection of downloaded and scanned infographic images. Another set of scanned document pages from the University of Washington document image database were used to demonstrate how the system output can be used by other applications. The results obtained confirm the practical value of the system.
Coupling design and performance analysis of rim-driven integrated motor propulsor
(2016)

Coupling design of rim-driven integrated motor propulsor for vessels and underwater vehicle is presented in this paper. The main characteristic of integrated motor propulsor is that the motor is integrated in the duct of propulsor. So the coupling design of motor and propulsor is the key to the overall design. Considering the influence of the motor and duct size, the propeller was designed, and the CFD Method was used to analyze the hydrodynamic performance of the propulsor. Based on the air-gap magnetic field of permanent magnet motor and the equivalent magnetic circuit, the integrated motor electromagnetic model was proposed, and the finite element method was used to analyze the motor electromagnetic field. Finally, the simulation of the integrated motor starting process with the load of propulsor torque was carried out, and the results meets the design specifications.
Deep keyphrase generation with a convolutional sequence to sequence model
(2017)

Keyphrases can provide highly condensed and valuable information that allows users to quickly acquire the main ideas. Most previous studies realize the automatic keyphrase extraction through dividing the source text into multiple chunks and then rank and select the most suitable ones. These approaches ignore the deep semantics behind the text and could not predict the keyphrases not appearing in the source text. A sequence to sequence model to generate keyphrases from vocabulary could solve the issues above. However, traditional sequence to sequence model based on recurrent neural network(RNN) suffers from low efficiency problem. We propose an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations can be completely parallelized over all elements so as to better exploit the GPU hardware. Our use of gated linear units alleviates gradient propagation and we equip each decoder layer with a separate attention model. Moreover, we incorporate a copying mechanism to handle out-of-vocabulary phrases. In experiments, we evaluate our model on six datasets, and our proposed model is demonstrated to outperform state-of-the-art baseline models consistently and significantly, both on extracting the keyphrases existing in the source text and generating the absent keyphrases based on the sematic meaning of the text.
Evolutionary timeline summarization: a balanced optimization framework via iterative substitution
(2011)

Classic news summarization plays an important role with the exponential document growth on the Web. Many approaches are proposed to generate summaries but seldom simultaneously consider evolutionary characteristics of news plus to traditional summary elements. Therefore, we present a novel framework for the web mining problem named Evolutionary Timeline Summarization (ETS). Given the massive collection of time-stamped web documents related to a general news query, ETS aims to return the evolution trajectory along the timeline, consisting of individual but correlated summaries of each date, emphasizing relevance, coverage, coherence and cross-date diversity. ETS greatly facilitates fast news browsing and knowledge comprehension and hence is a necessity. We formally formulate the task as an optimization problem via iterative substitution from a set of sentences to a subset of sentences that satisfies the above requirements, balancing coherence/diversity measurement and local/global summary quality. The optimized substitution is iteratively conducted by incorporating several constraints until convergence. We develop experimental systems to evaluate on 6 instinctively different datasets which amount to 10251 documents. Performance comparisons between different system-generated timelines and manually created ones by human editors demonstrate the effectiveness of our proposed framework in terms of ROUGE metrics.
Joint Inference for Event Timeline Construction
(2012)

This paper addresses the task of constructing a timeline of events mentioned in a given text. To accomplish that, we present a novel representation of the temporal structure of a news article based on time intervals. We then present an algorithmic approach that jointly optimizes the temporal structure by coupling local classifiers that predict associations and temporal relations between pairs of temporal entities with global constraints. Moreover, we present ways to leverage knowledge provided by event coreference to further improve the system performance. Overall, our experiments show that the joint inference model significantly outperformed the local classifiers by 9.2% of relative improvement in F1. The experiments also suggest that good event coreference could make remarkable contribution to a robust event timeline construction system.
Text classification and named entities for new event detection
(2004)

New Event Detection is a challenging task that still offers scope for great improvement after years of effort. In this paper we show how performance on New Event Detection (NED) can be improved by the use of text classification techniques as well as by using named entities in a new way. We explore modifications to the document representation in a vector space-based NED system. We also show that addressing named entities preferentially is useful only in certain situations. A combination of all the above results in a multi-stage NED system that performs much better than baseline single-stage NED systems.
Temporal Summaries of News Topics
(2001)

We discuss technology to help a person monitor changes in news coverage over time. We define temporal summaries of news stories as extracting a single sentence from each event within a news topic, where the stories are presented one at a time and sentences from a story must be ranked before the next story can be considered. We explain a method for evaluation, and describe an evaluation corpus that we have built. We also propose several methods for constructing temporal summaries and evaluate their effectiveness in comparison to degenerate cases. We show that simple approaches are effective, but that the problem is far from solved.
A 2.6GHz band 537W peak power GaN HEMT asymmetric Doherty amplifier with 48% drain efficiency at 7dB
(2012)

A 537W saturation output power (Psat) asymmetric Doherty amplifier for 2.6GHz band was successfully developed. The main and peak amplifiers were implemented with Psat of 210W and 320W GaN HEMTs. The newly developed 320W GaN HEMT consists of a single GaN die, both input and output partial match networks and a compact package. Its output matching network was tuned to inverse class F and a single-ended 320W GaN HEMT achieved higher than 61.8% drain efficiency from 2.4GHz to 2.7GHz. The 210W and 320W GaN HEMTs asymmetric Doherty amplifier exhibited 57.3dBm (537W) Psat and 48% drain efficiency with −50.6dBc ACLR at 50.3dBm (107W) average output power using a 4-carrier W-CDMA signal and commercially available digital pre-distortion system. These excellent performances show the good suitability for 2.6GHz band basestations.
A survey of automated material handling systems in 300-mm SemiconductorFabs
(2006)

The fast-paced developments and technological breakthroughs in the semiconductor manufacturing industry elevates the importance of optimum utilization of resources. The newer 300-mm wafers fabs place a high level of emphasis on increasing yield and reducing cycle times. Automated material handling systems are importanttools that help us achieve these objectives. In addition, due to the increased weight and size of 300-mm wafers, an automated material handling system isa must for a 300-mm manufacturing facility. This paper discusses various approaches for automated materials handling in semiconductor manufacturing industries.
Nuclear CDKs Drive Smad Transcriptional Activation and Turnover in BMP and TGF-β Pathways
(2009)

TGF-beta and BMP receptor kinases activate Smad transcription factors by C-terminal phosphorylation. We have identified a subsequent agonist-induced phosphorylation that plays a central dual role in Smad transcriptional activation and turnover. As receptor-activated Smads form transcriptional complexes, they are phosphorylated at an interdomain linker region by CDK8 and CDK9, which are components of transcriptional mediator and elongation complexes. These phosphorylations promote Smad transcriptional action, which in the case of Smad1 is mediated by the recruitment of YAP to the phosphorylated linker sites. An effector of the highly conserved Hippo organ size control pathway, YAP supports Smad1-dependent transcription and is required for BMP suppression of neural differentiation of mouse embryonic stem cells. The phosphorylated linker is ultimately recognized by specific ubiquitin ligases, leading to proteasome-mediated turnover of activated Smad proteins. Thus, nuclear CDK8/9 drive a cycle of Smad utilization and disposal that is an integral part of canonical BMP and TGF-beta pathways.